995 resultados para Fusion food


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Feeding 9-10billion people by 2050 and preventing dangerous climate change are two of the greatest challenges facing humanity. Both challenges must be met while reducing the impact of land management on ecosystem services that deliver vital goods and services, and support human health and well-being. Few studies to date have considered the interactions between these challenges. In this study we briefly outline the challenges, review the supply- and demand-side climate mitigation potential available in the Agriculture, Forestry and Other Land Use AFOLU sector and options for delivering food security. We briefly outline some of the synergies and trade-offs afforded by mitigation practices, before presenting an assessment of the mitigation potential possible in the AFOLU sector under possible future scenarios in which demand-side measures codeliver to aid food security. We conclude that while supply-side mitigation measures, such as changes in land management, might either enhance or negatively impact food security, demand-side mitigation measures, such as reduced waste or demand for livestock products, should benefit both food security and greenhouse gas (GHG) mitigation. Demand-side measures offer a greater potential (1.5-15.6Gt CO2-eq. yr(-1)) in meeting both challenges than do supply-side measures (1.5-4.3Gt CO2-eq. yr(-1) at carbon prices between 20 and 100US$ tCO(2)-eq. yr(-1)), but given the enormity of challenges, all options need to be considered. Supply-side measures should be implemented immediately, focussing on those that allow the production of more agricultural product per unit of input. For demand-side measures, given the difficulties in their implementation and lag in their effectiveness, policy should be introduced quickly, and should aim to codeliver to other policy agenda, such as improving environmental quality or improving dietary health. These problems facing humanity in the 21st Century are extremely challenging, and policy that addresses multiple objectives is required now more than ever.

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Mitochondrial biogenesis and morphological changes are associated with tissue-specific functional demand, but the factors and pathways that regulate these processes have not been completely identified. A lack of mitochondrial fusion has been implicated in various developmental and pathological defects. The spatiotemporal regulation of mitochondrial fusion in a tissue such as muscle is not well understood. Here, we show in Drosophila indirect flight muscles (IFMs) that the nuclear-encoded mitochondrial inner membrane fusion gene, Opa1-like, is regulated in a spatiotemporal fashion by the transcription factor/co-activator Erect wing (Ewg). In IFMs null for Ewg, mitochondria undergo mitophagy and/or autophagy accompanied by reduced mitochondrial functioning and muscle degeneration. By following the dynamics of mitochondrial growth and shape in IFMs, we found that mitochondria grow extensively and fuse during late pupal development to form the large tubular mitochondria. Our evidence shows that Ewg expression during early IFM development is sufficient to upregulate Opa1-like, which itself is a requisite for both late pupal mitochondrial fusion and muscle maintenance. Concomitantly, by knocking down Opa1-like during early muscle development, we show that it is important for mitochondrial fusion, muscle differentiation and muscle organization. However, knocking down Opa1-like, after the expression window of Ewg did not cause mitochondrial or muscle defects. This study identifies a mechanism by which mitochondrial fusion is regulated spatiotemporally by Ewg through Opa1-like during IFM differentiation and growth.

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Non-human primate populations, other than responding appropriately to naturally occurring challenges, also need to cope with anthropogenic factors such as environmental pollution, resource depletion, and habitat destruction. Populations and individuals are likely to show considerable variations in food extraction abilities, with some populations and individuals more efficient than others at exploiting a set of resources. In this study, we examined among urban free-ranging bonnet macaques, Macaca radiata (a) local differences in food extraction abilities, (b) between-individual variation and within-individual consistency in problem-solving success and the underlying problem-solving characteristics, and (c) behavioral patterns associated with higher efficiency in food extraction. When presented with novel food extraction tasks, the urban macaques having more frequent exposure to novel physical objects in their surroundings, extracted food material from PET bottles and also solved another food extraction task (i.e., extracting an orange from a wire mesh box), more often than those living under more natural conditions. Adults solved the tasks more frequently than juveniles, and females more frequently than males. Both solution-technique and problem-solving characteristics varied across individuals but remained consistent within each individual across the successive presentations of PET bottles. The macaques that solved the tasks showed lesser within-individual variation in their food extraction behavior as compared to those that failed to solve the tasks. A few macaques appropriately modified their problem-solving behavior in accordance with the task requirements and solved the modified versions of the tasks without trial-and-error learning. These observations are ecologically relevant - they demonstrate considerable local differences in food extraction abilities, between-individual variation and within-individual consistency in food extraction techniques among free-ranging bonnet macaques, possibly affecting the species' local adaptability and resilience to environmental changes.

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Numerous algorithms have been proposed recently for sparse signal recovery in Compressed Sensing (CS). In practice, the number of measurements can be very limited due to the nature of the problem and/or the underlying statistical distribution of the non-zero elements of the sparse signal may not be known a priori. It has been observed that the performance of any sparse signal recovery algorithm depends on these factors, which makes the selection of a suitable sparse recovery algorithm difficult. To take advantage in such situations, we propose to use a fusion framework using which we employ multiple sparse signal recovery algorithms and fuse their estimates to get a better estimate. Theoretical results justifying the performance improvement are shown. The efficacy of the proposed scheme is demonstrated by Monte Carlo simulations using synthetic sparse signals and ECG signals selected from MIT-BIH database.

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Recently, it has been shown that fusion of the estimates of a set of sparse recovery algorithms result in an estimate better than the best estimate in the set, especially when the number of measurements is very limited. Though these schemes provide better sparse signal recovery performance, the higher computational requirement makes it less attractive for low latency applications. To alleviate this drawback, in this paper, we develop a progressive fusion based scheme for low latency applications in compressed sensing. In progressive fusion, the estimates of the participating algorithms are fused progressively according to the availability of estimates. The availability of estimates depends on computational complexity of the participating algorithms, in turn on their latency requirement. Unlike the other fusion algorithms, the proposed progressive fusion algorithm provides quick interim results and successive refinements during the fusion process, which is highly desirable in low latency applications. We analyse the developed scheme by providing sufficient conditions for improvement of CS reconstruction quality and show the practical efficacy by numerical experiments using synthetic and real-world data. (C) 2013 Elsevier B.V. All rights reserved.

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The low level, denuded, laterite landscape of coastal Uttara Kannada has a rich diversity of monsoon herbs, including threatened and newly discovered ones. Our study reveals that honey bees congregate on the ephemeral herb community of Utricularias, Eriocaulons and Impatiens during their gregarious monsoon flowering period. Apis dorsata had highest visitations on Utricularias, Impatiens and Flacourtia indica, whereas Trigona preferred Eriocaulons. Laterite herb flora merits conservation efforts as a keystone food resource for the insect community, especially for honey bees.

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This article highlights different synthetic strategies for the preparation of colloidal heterostructured nanocrystals, where at least one component of the constituent nanostructure is a semiconductor. Growth of shell material on a core nanocrystal acting as a seed for heterogeneous nucleation of the shell has been discussed. This seeded-growth technique, being one of the most heavily explored mechanisms, has already been discussed in many other excellent review articles. However, here our discussion has been focused differently based on composition (semiconductor@semiconductor, magnet@semiconductor, metal@semiconductor and vice versa), shape anisotropy of the shell growth, and synthetic methodology such as one-step vs. multi-step. The relatively less explored strategy of preparing heterostructures via colloidal sintering of different nanostructures, known as nanocrystal-fusion, has been reviewed here. The ion-exchange strategy, which has recently attracted huge research interest, where compositional tuning of nanocrystals can be achieved by exchanging either the cation or anion of a nanocrystal, has also been discussed. Specifically, controlled partial ion exchange has been critically reviewed as a viable synthetic strategy for the fabrication of heterostructures. Notably, we have also included the very recent methodology of utilizing inorganic ligands for the fabrication of heterostructured colloidal nanocrystals. This unique strategy of inorganic ligands has appeared as a new frontier for the synthesis of heterostructures and is reviewed in detail here for the first time. In all these cases, recent developments have been discussed with greater detail to add upon the existing reviews on this broad topic of semiconductor-based colloidal heterostructured nanocrystals.

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Cryosorption pump is the only possible device to pump helium, hydrogen and its isotopes in fusion environment, such as high magnetic field and high plasma temperatures. Activated carbons are known to be the most suitable adsorbent in the development of cryosorption pumps. For this purpose, the data of adsorption characteristics of activated carbons in the temperature range 4.5 K to 77 K are needed, but are not available in the literature. For obtaining the above data, a commercial micro pore analyzer operating at 77 K has been integrated with a two stage GM cryocooler, which enables the cooling of the sample temperature down to 4.5 K. A heat switch mounted between the second stage cold head and the sample chamber helps to raise the sample chamber temperature to 77 K without affecting the performance of the cryocooler. The detailed description of this system is presented elsewhere. This paper presents the results of experimental studies of adsorption isotherms measured on different types of activated carbons in the form of granules, globules, flake knitted and non-woven types in the temperature range 4.5 K to 10 K using Helium gas as the adsorbate. The above results are analyzed to obtain the pore size distributions and surface areas of the activated carbons. The effect of adhesive used for bonding the activated carbons to the panels is also studied. These results will be useful to arrive at the right choice of activated carbon to be used for the development of cryosorption pumps.

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This paper proposes an optical flow algorithm by adapting Approximate Nearest Neighbor Fields (ANNF) to obtain a pixel level optical flow between image sequence. Patch similarity based coherency is performed to refine the ANNF maps. Further improvement in mapping between the two images are obtained by fusing bidirectional ANNF maps between pair of images. Thus a highly accurate pixel level flow is obtained between the pair of images. Using pyramidal cost optimization, the pixel level optical flow is further optimized to a sub-pixel level. The proposed approach is evaluated on the middlebury dataset and the performance obtained is comparable with the state of the art approaches. Furthermore, the proposed approach can be used to compute large displacement optical flow as evaluated using MPI Sintel dataset.

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Food industries like biscuit and confectionary use significant amount of fossil fuel for thermal energy. Biscuit manufacturing in India is carried out both by organized and unorganized sector. The ratio of organized to unorganized sector is 60 : 40 (1). The total biscuit manufacturing in the organized sector India in 2008 was about 1.7 million metric tons (1). Accounting for the unorganized sector in India, the total biscuit manufacturing would have been about 2.9 million metric tons/annum. A typical biscuit baking is carried in a long tunnel kiln with varying temperature in different zones. Generally diesel is used to provide the necessary heat energy for the baking purpose, with temperature ranging from 190 C in the drying zone to about 300 C in the baking area and has to maintain in the temperature range of +/- 5 C. Typical oil consumption is about 40 litres per ton of biscuit production. The paper discusses the experience in substituting about 120 lts per hour kiln for manufacturing about 70 tons of biscuit daily. The system configuration consists of a 500 kg/hr gasification system comprising of a reactor, multicyclone, water scrubbers, and two blowers for maintaining the constant gas pressure in the header before the burners. Cold producer gas is piped to the oven located about 200 meters away from the gasifier. Fuel used in the gasification system is coconut shells. All the control system existing on the diesel burner has been suitably adapted for producer gas operation to maintain the total flow, A/F control so as to maintain the temperature. A total of 7 burners are used in different zones. Over 17000 hour of operation has resulted in replacing over 1800 tons of diesel over the last 30 months. The system operates for over 6 days a week with average operational hours of 160. It has been found that on an average 3.5 kg of biomass has replaced one liter of diesel.

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It is well known that wrist pulse signals contain information about the status of health of a person and hence diagnosis based on pulse signals has assumed great importance since long time. In this paper the efficacy of signal processing techniques in extracting useful information from wrist pulse signals has been demonstrated by using signals recorded under two different experimental conditions viz. before lunch condition and after lunch condition. We have used Pearson's product-moment correlation coefficient, which is an effective measure of phase synchronization, in making a statistical analysis of wrist pulse signals. Contour plots and box plots are used to illustrate various differences. Two-sample t-tests show that the correlations show statistically significant differences between the groups. Results show that the correlation coefficient is effective in distinguishing the changes taking place after having lunch. This paper demonstrates the ability of the wrist pulse signals in detecting changes occurring under two different conditions. The study assumes importance in view of limited literature available on the analysis of wrist pulse signals in the case of food intake and also in view of its potential health care applications.

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This paper studies a pilot-assisted physical layer data fusion technique known as Distributed Co-Phasing (DCP). In this two-phase scheme, the sensors first estimate the channel to the fusion center (FC) using pilots sent by the latter; and then they simultaneously transmit their common data by pre-rotating them by the estimated channel phase, thereby achieving physical layer data fusion. First, by analyzing the symmetric mutual information of the system, it is shown that the use of higher order constellations (HOC) can improve the throughput of DCP compared to the binary signaling considered heretofore. Using an HOC in the DCP setting requires the estimation of the composite DCP channel at the FC for data decoding. To this end, two blind algorithms are proposed: 1) power method, and 2) modified K-means algorithm. The latter algorithm is shown to be computationally efficient and converges significantly faster than the conventional K-means algorithm. Analytical expressions for the probability of error are derived, and it is found that even at moderate to low SNRs, the modified K-means algorithm achieves a probability of error comparable to that achievable with a perfect channel estimate at the FC, while requiring no pilot symbols to be transmitted from the sensor nodes. Also, the problem of signal corruption due to imperfect DCP is investigated, and constellation shaping to minimize the probability of signal corruption is proposed and analyzed. The analysis is validated, and the promising performance of DCP for energy-efficient physical layer data fusion is illustrated, using Monte Carlo simulations.

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We propose apractical, feature-level and score-level fusion approach by combining acoustic and estimated articulatory information for both text independent and text dependent speaker verification. From a practical point of view, we study how to improve speaker verification performance by combining dynamic articulatory information with the conventional acoustic features. On text independent speaker verification, we find that concatenating articulatory features obtained from measured speech production data with conventional Mel-frequency cepstral coefficients (MFCCs) improves the performance dramatically. However, since directly measuring articulatory data is not feasible in many real world applications, we also experiment with estimated articulatory features obtained through acoustic-to-articulatory inversion. We explore both feature level and score level fusion methods and find that the overall system performance is significantly enhanced even with estimated articulatory features. Such a performance boost could be due to the inter-speaker variation information embedded in the estimated articulatory features. Since the dynamics of articulation contain important information, we included inverted articulatory trajectories in text dependent speaker verification. We demonstrate that the articulatory constraints introduced by inverted articulatory features help to reject wrong password trials and improve the performance after score level fusion. We evaluate the proposed methods on the X-ray Microbeam database and the RSR 2015 database, respectively, for the aforementioned two tasks. Experimental results show that we achieve more than 15% relative equal error rate reduction for both speaker verification tasks. (C) 2015 Elsevier Ltd. All rights reserved.